Adaptive Optimizers

Adaptive optimizers dynamically adjust learning rates during training, aiming to improve the efficiency and generalization of deep learning models compared to traditional methods like SGD. Current research focuses on enhancing their stability, convergence rates, and generalization performance, particularly within the context of ResNets, Vision Transformers, and language models like GPT-2, often by incorporating techniques like adaptive friction, factorized momentum, and novel preconditioning matrices. These advancements are significant because they lead to faster training, improved model accuracy, and more efficient use of computational resources across diverse machine learning applications.